Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms
Walaa H. Elashmawi, Adel Djellal, Alaa Sheta, Salim Surani, Sultan Aljahdali · 2024
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Description
Walaa Elashmawi and four co-authors compare machine learning methods for diagnosing lung disease in a 2024 paper. Chronic obstructive pulmonary disease blocks airflow, causes coughing and wheeze, and raises the risk of heart disease and lung cancer. Earlier, cheaper flagging of likely cases could speed proper testing.
The team ran eight standard classifiers, from logistic regression to a neural network, on records of about sixteen hundred patients referred for lung function testing. A random forest did best, reaching about eighty-two percent accuracy in training and seventy percent in testing, with the strongest combined scores. Other models landed in a similar band, matching earlier studies. The authors are measured: the models support, not replace, the breathing test that confirms diagnosis. Still, a triage model that sorts referrals could focus scarce testing capacity where it counts.
Catalog record
| CREATOR | Walaa H. Elashmawi, Adel Djellal, Alaa Sheta, Salim Surani, Sultan Aljahdali |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |